Health Information–Seeking Behaviors of Family Caregivers: Analysis of the Health Information National Trends Survey
Bibliographic record
Abstract
BACKGROUND: The growing population of aging adults relies on informal caregivers to help meet their health care needs, get help with decision making, and gather health information. OBJECTIVE: The objective of this study was to examine health information-seeking behaviors among caregivers and to identify caregiver characteristics that contribute to difficulty in seeking health information. METHODS: Data from the Health Information National Trends Survey 5, Cycle 1 (N=3181) were used to compare health information seeking of caregivers (n=391) with noncaregivers (n=2790). RESULTS: Caregivers sought health information for themselves and others using computers, smartphones, or other electronic means more frequently than noncaregivers. Caregivers born outside of the United States reported greater difficulty seeking health information (beta=.42; P=.02). Nonwhite caregivers (beta =-.33; P=.03), those with less education (beta =-.35; P=.02), those with private insurance (beta =-.37; P=.01), and those without a regular health care provider (beta =-.35; P=.01) had less confidence seeking health information. Caregivers with higher income had more confidence (beta =.12; P≤.001) seeking health information. CONCLUSIONS: This study highlights the prevalence of electronic means to find health information among caregivers. Notable differences in difficulty and confidence in health information seeking exist between caregivers, indicating the need for more attention to the socioeconomic status and caregivers born outside of the United States. Findings can guide efforts to optimize caregivers' health information-seeking experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".